题 目： Heat Transfer Studies: from “Feature-based” to “Rule-based”

报告人： 杨力 上海交通大学副教授

时 间： 2021 年 7 月 11 日（周日） 上午 10:00~11:00

地 点： 1号巨构5078

邀请人： 马挺 教授

报告简介：Geometry definition, heat transfer evaluation and design optimization are the main processes of heat transfer structure research. At present, the bottlenecks of heat transfer performance in many applications mainly come from these three aspects because of the constraints on design caused by "feature thinking". This report will introduce the limitations of feature-based traditional research methods including parametric modeling, data regression and genetic optimization, and the advantages of rule-based research methods including self-organizing geometry, operator neural networks and reinforcement learning. Taking the typical convective heat transfer problem as an example, it will be explained the significance of "rule thinking" for the study of heat transfer structure. The report shall include three parts. In the first part, in view of the limitations of parametric modeling, the presenters explored the mechanism of geometry and its programmable approach, and analyze the compatibility of geometry with flow field and scalar field through the geometric shape design method based on self-organization theory. In the second part, the presenters shall talk about the limitations of regression deep learning which is based on image processing for temperature field reconstruction. Besides, a rule-based operator deep learning method shall be proposed for multi-size, multi-working conditions, and variable topological geometry heat transfer information learning method. In the third part, the feasibility of using the intelligent body for spatial structure design is discussed in view of the limitations of parameter optimization. At the moment, the versatility of the artificial body for optimization problems is analyzed. Reinforcement learning methods are applied to significantly accelerate the optimization speed of the structure, and to a certain extent avoid the dimensional explosion.

报告人简介： 杨力，上海交通大学副教授。2015年于清华大学获得博士学位，2018年完成美国匹兹堡大学的博士后研究。先后在清华大学、美国弗吉尼亚理工大学、美国匹兹堡大学和上海交通大学开展热端部件冷却技术的前沿研究。在冲击冷却、发散冷却、金属增材制造和机器学习等研究方向发表多篇国际期刊论文，并参与多项美国能源部的增材制造先进冷却技术项目研究。2018年加入上海交通大学后，主要集中于航空发动机和燃气轮机热端部件冷却相关的复杂结构传热优化、传热问题机器学习和增材制造。